If you want to become a software engineer, but don’t know where to start, let’s save you the suspense: it’s algorithms and data structures.

Once you get the gist of these pillars of programming, you’ll start seeing them everywhere. And the more algorithms and data structures you learn, the more they’ll serve as jet fuel for your career as a software engineer.

To get you started, let’s first take a deep dive into Search and Sort, two classes of algorithms you can’t live without. Then let’s do a quick survey of the rest of the landscape that includes trees, graphs, dynamic programming and tons more.


Roughly speaking, there are two categories of search algorithms you’ll need to know right away: linear and binary. Depth First Search (DFS) and Breadth First Search (BFS) are also super-important, but we’ll save them for the graph traversal section below.

Linear search

The linear and binary algorithms are named as such to describe how long (time complexity) a search is going to take based on the size of the input that is being search.

For example, with linear search algorithms, if you have 100 items to search then the worst case scenario would require that you look at every item in the input before you came across your desired value. It is called linear because the time is takes to search is exactly correlated with the amount of items in the search (100 items/input =100 checks/complexity)

In short, linear = simple (there is nothing clever about the algorithm). For example: imagine you’re trying find your friend Lin among a line of people standing in no particular order. You already know what Lin looks like, so you simply have to look at each person, one by one, in sequence, until you recognize or fail to recognize Lin. That’s it. In doing so, you are following the linear search algorithm

Binary search

Binary search gets its name because the word binary means “of or relating to 2 things” and the algorithm works by splitting the input into two parts until it finds the item that is being searched. One half contains the search item and the other half doesn’t. The process continues until the spot where the input is split becomes the item that is being searched. Binary search is basically just a highly disciplined approach to the process of elimination. It’s faster than linear search, but it only works with ordered sequences.

An example should make this more clear. Suppose you’re trying to find your friend Bin (who is 5’5’’) in a single-file line of people that have been ordered by height from left to right, shortest to tallest. It’s a really long line, and you don’t have time to go one-by-one through the whole thing, comparing everyone’s height to Bin’s. What can you do?

Enter binary search. You select the person in the very middle of the line, and measure their height. They’re 5’7’’. So right away you know that this person, along with everyone to their right, is not Bin. Now that you’ve cut your problem in half, you turn your attention to the remainder of the line and pick the middle person again. They’re 5’4’’. So you can rule out that person and anyone to their left, cutting the problem in half again. And so on. After just five or six of these splits, you quickly home in on Bin in a fraction of the time it took you to find Lin. In doing so, you have followed the binary search algorithm.


Ordering, otherwise know as sorting, lists of items is one of the most common programming tasks you’ll do as a developer. Here we look at two of the most useful sorting algorithms: MergeSort and QuickSort.


Let’s suppose that rather than coming across the ordered line of people in the example above, you need to create an ordered line of people out of an unordered group. You don’t have much time, so you come up with a strategy to speed things up.

You first have the group of people, which are all huddled together, divide into two. Then you have each of the two groups divide into two again, and so on, until you are dealing entirely with individuals. You then begin to pair the individuals up, and have the taller of the two in each pair stand to the right of the other one. Pretty soon everyone is organized into these left-right ordered pairs.

Next, you start merging the ordered pairs into ordered groups of four; then merging the ordered groups of four into ordered groups of eight; and so on. Finally, you find that you have a complete, height-ordered line of people, just like the one you encountered above. Without knowing it, you have followed the MergeSort algorithm to accomplish your feat.


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